Description: Many modern human genomes retain DNA inherited from interbreeding with archaic hominins, such as Neandertals, yet the influence of this admixture on human traits is largely unknown. We analyzed the contribution of common Neandertal variants to over 1000 electronic health record (EHR)-derived phenotypes in ~28,000 adults of European ancestry. We discovered and replicated associations of Neandertal alleles with neurological, psychiatric, immunological, and dermatological phenotypes. Neandertal alleles together explained a significant fraction of the variation in risk for depression and skin lesions resulting from sun exposure (actinic keratosis), and individual Neandertal alleles were significantly associated with specific human phenotypes, including hypercoagulation and tobacco use. Our results establish that archaic admixture influences disease risk in modern humans, provide hypotheses about the effects of hundreds of Neandertal haplotypes, and demonstrate the utility of EHR data in evolutionary analyses.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Simonti, Corinne N -- Vernot, Benjamin -- Bastarache, Lisa -- Bottinger, Erwin -- Carrell, David S -- Chisholm, Rex L -- Crosslin, David R -- Hebbring, Scott J -- Jarvik, Gail P -- Kullo, Iftikhar J -- Li, Rongling -- Pathak, Jyotishman -- Ritchie, Marylyn D -- Roden, Dan M -- Verma, Shefali S -- Tromp, Gerard -- Prato, Jeffrey D -- Bush, William S -- Akey, Joshua M -- Denny, Joshua C -- Capra, John A -- 1K22LM011938/LM/NLM NIH HHS/ -- 1R01GM114128/GM/NIGMS NIH HHS/ -- 5T32EY021453/EY/NEI NIH HHS/ -- R01GM110068/GM/NIGMS NIH HHS/ -- R01LM010685/LM/NLM NIH HHS/ -- U01HG004438/HG/NHGRI NIH HHS/ -- U01HG004608/HG/NHGRI NIH HHS/ -- U01HG004609/HG/NHGRI NIH HHS/ -- U01HG004610/HG/NHGRI NIH HHS/ -- U01HG006378/HG/NHGRI NIH HHS/ -- U01HG006379/HG/NHGRI NIH HHS/ -- U01HG006380/HG/NHGRI NIH HHS/ -- U01HG006382/HG/NHGRI NIH HHS/ -- U01HG006385/HG/NHGRI NIH HHS/ -- U01HG006388/HG/NHGRI NIH HHS/ -- U01HG006389/HG/NHGRI NIH HHS/ -- U01HG008657/HG/NHGRI NIH HHS/ -- U01HG04599/HG/NHGRI NIH HHS/ -- U01HG04603/HG/NHGRI NIH HHS/ -- New York, N.Y. -- Science. 2016 Feb 12;351(6274):737-41. doi: 10.1126/science.aad2149.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA. ; Department of Genome Sciences, University of Washington, Seattle, WA, USA. ; Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. ; Mount Sinai School of Medicine, New York, NY, USA. ; Department of Medicine (Medical Genetics), University of Washington Medical Center, Seattle, WA, USA. ; Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA. ; Department of Genome Sciences, University of Washington, Seattle, WA, USA. Department of Medicine (Medical Genetics), University of Washington Medical Center, Seattle, WA, USA. ; Center for Human Genetics, Marshfield Clinic, Marshfield, WI, USA. ; Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA. ; Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA. ; Division of Health Sciences Research, Mayo Clinic, Rochester, MN, USA. ; Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, USA. Biomedical and Translational Informatics, Geisinger Health System, Danville, PA, USA. ; Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA. Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. Department of Medicine, Vanderbilt University, Nashville, TN, USA. Department of Pharmacology, Vanderbilt University, Nashville, TN, USA. ; Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, USA. ; Weis Center for Research, Geisinger Health System, Danville, PA, USA. Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Health Science, Stellenbosch University, Tygerberg, South Africa. ; Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA. ; Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA. Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. Department of Medicine, Vanderbilt University, Nashville, TN, USA. ; Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA. Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA. Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26912863" target="_blank"〉PubMed〈/a〉

Description: A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA). Here we performed a genome-wide association study meta-analysis in a total of 〉100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating approximately 10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2 - 4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation, cis-acting expression quantitative trait loci and pathway analyses--as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes--to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3944098/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3944098/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Okada, Yukinori -- Wu, Di -- Trynka, Gosia -- Raj, Towfique -- Terao, Chikashi -- Ikari, Katsunori -- Kochi, Yuta -- Ohmura, Koichiro -- Suzuki, Akari -- Yoshida, Shinji -- Graham, Robert R -- Manoharan, Arun -- Ortmann, Ward -- Bhangale, Tushar -- Denny, Joshua C -- Carroll, Robert J -- Eyler, Anne E -- Greenberg, Jeffrey D -- Kremer, Joel M -- Pappas, Dimitrios A -- Jiang, Lei -- Yin, Jian -- Ye, Lingying -- Su, Ding-Feng -- Yang, Jian -- Xie, Gang -- Keystone, Ed -- Westra, Harm-Jan -- Esko, Tonu -- Metspalu, Andres -- Zhou, Xuezhong -- Gupta, Namrata -- Mirel, Daniel -- Stahl, Eli A -- Diogo, Dorothee -- Cui, Jing -- Liao, Katherine -- Guo, Michael H -- Myouzen, Keiko -- Kawaguchi, Takahisa -- Coenen, Marieke J H -- van Riel, Piet L C M -- van de Laar, Mart A F J -- Guchelaar, Henk-Jan -- Huizinga, Tom W J -- Dieude, Philippe -- Mariette, Xavier -- Bridges, S Louis Jr -- Zhernakova, Alexandra -- Toes, Rene E M -- Tak, Paul P -- Miceli-Richard, Corinne -- Bang, So-Young -- Lee, Hye-Soon -- Martin, Javier -- Gonzalez-Gay, Miguel A -- Rodriguez-Rodriguez, Luis -- Rantapaa-Dahlqvist, Solbritt -- Arlestig, Lisbeth -- Choi, Hyon K -- Kamatani, Yoichiro -- Galan, Pilar -- Lathrop, Mark -- RACI consortium -- GARNET consortium -- Eyre, Steve -- Bowes, John -- Barton, Anne -- de Vries, Niek -- Moreland, Larry W -- Criswell, Lindsey A -- Karlson, Elizabeth W -- Taniguchi, Atsuo -- Yamada, Ryo -- Kubo, Michiaki -- Liu, Jun S -- Bae, Sang-Cheol -- Worthington, Jane -- Padyukov, Leonid -- Klareskog, Lars -- Gregersen, Peter K -- Raychaudhuri, Soumya -- Stranger, Barbara E -- De Jager, Philip L -- Franke, Lude -- Visscher, Peter M -- Brown, Matthew A -- Yamanaka, Hisashi -- Mimori, Tsuneyo -- Takahashi, Atsushi -- Xu, Huji -- Behrens, Timothy W -- Siminovitch, Katherine A -- Momohara, Shigeki -- Matsuda, Fumihiko -- Yamamoto, Kazuhiko -- Plenge, Robert M -- 20385/Arthritis Research UK/United Kingdom -- 79321/Canadian Institutes of Health Research/Canada -- K08-KAR055688A/PHS HHS/ -- K24 AR052403/AR/NIAMS NIH HHS/ -- P60 AR047785/AR/NIAMS NIH HHS/ -- R01 AR056768/AR/NIAMS NIH HHS/ -- R01 AR057108/AR/NIAMS NIH HHS/ -- R01 AR059648/AR/NIAMS NIH HHS/ -- R01 AR063759/AR/NIAMS NIH HHS/ -- R01-AR056291/AR/NIAMS NIH HHS/ -- R01-AR056768/AR/NIAMS NIH HHS/ -- R01-AR057108/AR/NIAMS NIH HHS/ -- R01-AR059648/AR/NIAMS NIH HHS/ -- R01-AR065944/AR/NIAMS NIH HHS/ -- R01AR063759-01A1/AR/NIAMS NIH HHS/ -- R21 AR056042/AR/NIAMS NIH HHS/ -- T15 LM007450/LM/NLM NIH HHS/ -- U01 GM092691/GM/NIGMS NIH HHS/ -- U01-GM092691/GM/NIGMS NIH HHS/ -- U19 HL065962/HL/NHLBI NIH HHS/ -- England -- Nature. 2014 Feb 20;506(7488):376-81. doi: 10.1038/nature12873. Epub 2013 Dec 25.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉1] Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. [2] Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. [3] Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA. ; 1] Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. [2] Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. [3] Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA. [4] Department of Statistics, Harvard University, Cambridge, Massachusetts 02138, USA. [5] Centre for Cancer Research, Monash Institute of Medical Research, Monash University, Clayton, Victoria 3800, Australia. ; 1] Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. [2] Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA. [3] Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA. ; 1] Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan. [2] Department of Rheumatology and Clinical immunology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan. ; Institute of Rheumatology, Tokyo Women's Medical University, Tokyo 162-0054, Japan. ; Laboratory for Autoimmune Diseases, Center for Integrative Medical Sciences, RIKEN, Yokohama 230-0045, Japan. ; Department of Rheumatology and Clinical immunology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan. ; Immunology Biomarkers Group, Genentech, South San Francisco, California 94080, USA. ; 1] Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA. [2] Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA. ; Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA. ; Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA. ; New York University Hospital for Joint Diseases, New York, New York 10003, USA. ; Department of Medicine, Albany Medical Center and The Center for Rheumatology, Albany, New York 12206, USA. ; Division of Rheumatology, Department of Medicine, New York, Presbyterian Hospital, College of Physicians and Surgeons, Columbia University, New York, New York 10032, USA. ; Department of Rheumatology and Immunology, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai 200003, China. ; Department of Pharmacology, Second Military Medical University, Shanghai 200433, China. ; 1] University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland 4072, Australia. [2] Queensland Brain Institute, The University of Queensland, Brisbane, Queensland 4072, Australia. ; 1] Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada. [2] Toronto General Research Institute, Toronto, Ontario M5G 2M9, Canada. [3] Department of Medicine, University of Toronto, Toronto, Ontario M5S 2J7, Canada. ; Department of Medicine, Mount Sinai Hospital and University of Toronto, Toronto M5S 2J7, Canada. ; Department of Genetics, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Groningen 9700 RB, the Netherlands. ; 1] Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA. [2] Estonian Genome Center, University of Tartu, Riia 23b, Tartu 51010, Estonia. [3] Division of Endocrinology, Children's Hospital, Boston, Massachusetts 02115, USA. ; Estonian Genome Center, University of Tartu, Riia 23b, Tartu 51010, Estonia. ; School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China. ; Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA. ; The Department of Psychiatry at Mount Sinai School of Medicine, New York, New York 10029, USA. ; 1] Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. [2] Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA. [3] Division of Endocrinology, Children's Hospital, Boston, Massachusetts 02115, USA. ; Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan. ; Department of Human Genetics, Radboud University Medical Centre, Nijmegen 6500 HB, the Netherlands. ; Department of Rheumatology, Radboud University Medical Centre, Nijmegen 6500 HB, the Netherlands. ; Department of Rheumatology and Clinical Immunology, Arthritis Center Twente, University Twente & Medisch Spectrum Twente, Enschede 7500 AE, the Netherlands. ; Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden 2300 RC, the Netherlands. ; Department of Rheumatology, Leiden University Medical Center, Leiden 2300 RC, the Netherlands. ; 1] Service de Rhumatologie et INSERM U699 Hopital Bichat Claude Bernard, Assistance Publique des Hopitaux de Paris, Paris 75018, France. [2] Universite Paris 7-Diderot, Paris 75013, France. ; Institut National de la Sante et de la Recherche Medicale (INSERM) U1012, Universite Paris-Sud, Rhumatologie, Hopitaux Universitaires Paris-Sud, Assistance Publique-Hopitaux de Paris (AP-HP), Le Kremlin Bicetre 94275, France. ; Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA. ; 1] Department of Genetics, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Groningen 9700 RB, the Netherlands. [2] Department of Rheumatology, Leiden University Medical Center, Leiden 2300 RC, the Netherlands. ; 1] AMC/University of Amsterdam, Amsterdam 1105 AZ, the Netherlands. [2] GlaxoSmithKline, Stevenage SG1 2NY, UK. [3] University of Cambridge, Cambridge CB2 1TN, UK. ; Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul 133-792, South Korea. ; Instituto de Parasitologia y Biomedicina Lopez-Neyra, CSIC, Granada 18100, Spain. ; Department of Rheumatology, Hospital Marques de Valdecilla, IFIMAV, Santander 39008, Spain. ; Hospital Clinico San Carlos, Madrid 28040, Spain. ; 1] Department of Public Health and Clinical Medicine, Umea University, Umea SE-901 87, Sweden. [2] Department of Rheumatology, Umea University, Umea SE-901 87, Sweden. ; 1] Channing Laboratory, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston 02115, Massachusetts, USA. [2] Section of Rheumatology, Boston University School of Medicine, Boston, Massachusetts 02118, USA. [3] Clinical Epidemiology Research and Training Unit, Boston University School of Medicine, Boston, Massachusetts 02118, USA. ; Centre d'Etude du Polymorphisme Humain (CEPH), Paris 75010, France. ; Universite Paris 13 Sorbonne Paris Cite, UREN (Nutritional Epidemiology Research Unit), Inserm (U557), Inra (U1125), Cnam, Bobigny 93017, France. ; McGill University and Genome Quebec Innovation Centre, Montreal, Quebec H3A 0G1 Canada. ; 1] Arthritis Research UK Epidemiology Unit, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9NT, UK. [2] National Institute for Health Research, Manchester Musculoskeletal Biomedical Research Unit, Central Manchester University Hospitals National Health Service Foundation Trust, Manchester Academic Health Sciences Centre, Manchester M13 9NT, UK. ; Arthritis Research UK Epidemiology Unit, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9NT, UK. ; Department of Clinical Immunology and Rheumatology & Department of Genome Analysis, Academic Medical Center/University of Amsterdam, Amsterdam 1105 AZ, the Netherlands. ; Division of Rheumatology and Clinical Immunology, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA. ; Rosalind Russell Medical Research Center for Arthritis, Division of Rheumatology, Department of Medicine, University of California San Francisco, San Francisco, California 94117, USA. ; Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. ; Unit of Statistical Genetics, Center for Genomic Medicine Graduate School of Medicine Kyoto University, Kyoto 606-8507, Japan. ; Laboratory for Genotyping Development, Center for Integrative Medical Sciences, RIKEN, Yokohama 230-0045, Japan. ; Department of Statistics, Harvard University, Cambridge, Massachusetts 02138, USA. ; Rheumatology Unit, Department of Medicine (Solna), Karolinska Institutet, Stockholm SE-171 76, Sweden. ; The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, New York 11030, USA. ; 1] Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. [2] Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. [3] Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA. [4] NIHR Manchester Musculoskeletal Biomedical, Research Unit, Central Manchester NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester M13 9NT, UK. ; 1] Section of Genetic Medicine, University of Chicago, Chicago, Illinois 60637, USA. [2] Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois 60637, USA. ; University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland 4072, Australia. ; Laboratory for Statistical Analysis, Center for Integrative Medical Sciences, RIKEN, Yokohama 230-0045, Japan. ; 1] Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan. [2] Core Research for Evolutional Science and Technology (CREST) program, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan. [3] Institut National de la Sante et de la Recherche Medicale (INSERM) Unite U852, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan. ; 1] Laboratory for Autoimmune Diseases, Center for Integrative Medical Sciences, RIKEN, Yokohama 230-0045, Japan. [2] Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/24390342" target="_blank"〉PubMed〈/a〉

Description: : Phenome-wide association studies (PheWAS) have been used to replicate known genetic associations and discover new phenotype associations for genetic variants. This PheWAS implementation allows users to translate ICD-9 codes to PheWAS case and control groups, perform analyses using these and/or other phenotypes with covariate adjustments and plot the results. We demonstrate the methods by replicating a PheWAS on rs3135388 (near HLA-DRB , associated with multiple sclerosis) and performing a novel PheWAS using an individual’s maximum white blood cell count (WBC) as a continuous measure. Our results for rs3135388 replicate known associations with more significant results than the original study on the same dataset. Our PheWAS of WBC found expected results, including associations with infections, myeloproliferative diseases and associated conditions, such as anemia. These results demonstrate the performance of the improved classification scheme and the flexibility of PheWAS encapsulated in this package. Availability and implementation: This R package is freely available under the Gnu Public License (GPL-3) from http://phewascatalog.org . It is implemented in native R and is platform independent. Contact: phewas@vanderbilt.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Objective— Rupture of abdominal aortic aneurysms causes a high morbidity and mortality in the elderly population. Platelet-rich thrombi form on the surface of aneurysms and may contribute to disease progression. In this study, we used a pharmacological approach to examine a role of platelets in established aneurysms induced by angiotensin II infusion into hypercholesterolemic mice. Approach and Results— Administration of the platelet inhibitors aspirin or clopidogrel bisulfate to established abdominal aortic aneurysms dramatically reduced rupture. These platelet inhibitors reduced abdominal aortic platelet and macrophage recruitment resulting in decreased active matrix metalloproteinase-2 and matrix metalloproteinase-9. Platelet inhibitors also resulted in reduced plasma concentrations of platelet factor 4, cytokines, and components of the plasminogen activation system in mice. To determine the validity of these findings in human subjects, a cohort of aneurysm patients were retrospectively analyzed using developed and validated algorithms in the electronic medical record database at Vanderbilt University. Similar to mice, administration of aspirin or P2Y 12 inhibitors was associated with reduced death among patients with abdominal aortic aneurysm. Conclusions— These results suggest that platelets contribute to abdominal aortic aneurysm progression and rupture.

Description: To identify novel genetic loci influencing interindividual variation in red blood cell (RBC) traits in African-Americans, we conducted a genome-wide association study (GWAS) in 2315 individuals, divided into discovery ( n = 1904) and replication ( n = 411) cohorts. The traits included hemoglobin concentration (HGB), hematocrit (HCT), RBC count, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and mean corpuscular hemoglobin concentration (MCHC). Patients were participants in the electronic MEdical Records and GEnomics (eMERGE) network and underwent genotyping of ~1.2 million single-nucleotide polymorphisms on the Illumina Human1M-Duo array. Association analyses were performed adjusting for age, sex, site, and population stratification. Three loci previously associated with resistance to malaria— HBB (11p15.4), HBA1/HBA2 (16p13.3), and G6PD (Xq28)—were associated ( P ≤ 1 x 10 –6 ) with RBC traits in the discovery cohort. The loci replicated in the replication cohort ( P ≤ 0.02), and were significant at a genome-wide significance level ( P 〈 5 x 10 –8 ) in the combined cohort. The proportions of variance in RBC traits explained by significant variants at these loci were as follows: rs7120391 (near HBB ) 1.3% of MCHC, rs9924561 (near HBA1/A2 ) 5.5% of MCV, 6.9% of MCH and 2.9% of MCHC, and rs1050828 (in G6PD ) 2.4% of RBC count, 2.9% of MCV, and 1.4% of MCH, respectively. We were not able to replicate loci identified by a previous GWAS of RBC traits in a European ancestry cohort of similar sample size, suggesting that the genetic architecture of RBC traits differs by race. In conclusion, genetic variants that confer resistance to malaria are associated with RBC traits in African-Americans.

Description: Background— Continued reductions in morbidity and mortality attributable to ischemic heart disease (IHD) require an understanding of the changing epidemiology of this disease. We hypothesized that we could use genetic correlations, which quantify the shared genetic architectures of phenotype pairs and extant risk factors from a historical prospective study to define the risk profile of a contemporary IHD phenotype. Methods and Results— We used 37 phenotypes measured in the ARIC study (Atherosclerosis Risk in Communities; n=7716, European ancestry subjects) and clinical diagnoses from an electronic health record (EHR) data set (n=19 093). All subjects had genome-wide single-nucleotide polymorphism genotyping. We measured pairwise genetic correlations (rG) between the ARIC and EHR phenotypes using linear mixed models. The genetic correlation estimates between the ARIC risk factors and the EHR IHD were modestly linearly correlated with hazards ratio estimates for incident IHD in ARIC (Pearson correlation [ r ]=0.62), indicating that the 2 IHD phenotypes had differing risk profiles. For comparison, this correlation was 0.80 when comparing EHR and ARIC type 2 diabetes mellitus phenotypes. The EHR IHD phenotype was most strongly correlated with ARIC metabolic phenotypes, including total:high-density lipoprotein cholesterol ratio (rG=–0.44, P =0.005), high-density lipoprotein (rG=–0.48, P =0.005), systolic blood pressure (rG=0.44, P =0.02), and triglycerides (rG=0.38, P =0.02). EHR phenotypes related to type 2 diabetes mellitus, atherosclerotic, and hypertensive diseases were also genetically correlated with these ARIC risk factors. Conclusions— The EHR IHD risk profile differed from ARIC and indicates that treatment and prevention efforts in this population should target hypertensive and metabolic disease.

Description: Genetic association studies often examine features independently, potentially missing subpopulations with multiple phenotypes that share a single cause. We describe an approach that aggregates phenotypes on the basis of patterns described by Mendelian diseases. We mapped the clinical features of 1204 Mendelian diseases into phenotypes captured from the electronic health record (EHR) and summarized this evidence as phenotype risk scores (PheRSs). In an initial validation, PheRS distinguished cases and controls of five Mendelian diseases. Applying PheRS to 21,701 genotyped individuals uncovered 18 associations between rare variants and phenotypes consistent with Mendelian diseases. In 16 patients, the rare genetic variants were associated with severe outcomes such as organ transplants. PheRS can augment rare-variant interpretation and may identify subsets of patients with distinct genetic causes for common diseases.

Description: Motivation: Sharing genomic data is crucial to support scientific investigation such as genome-wide association studies. However, recent investigations suggest the privacy of the individual participants in these studies can be compromised, leading to serious concerns and consequences, such as overly restricted access to data. Results: We introduce a novel cryptographic strategy to securely perform meta-analysis for genetic association studies in large consortia. Our methodology is useful for supporting joint studies among disparate data sites, where privacy or confidentiality is of concern. We validate our method using three multisite association studies. Our research shows that genetic associations can be analyzed efficiently and accurately across substudy sites, without leaking information on individual participants and site-level association summaries. Availability and implementation: Our software for secure meta-analysis of genetic association studies, SecureMA, is publicly available at http://github.com/XieConnect/SecureMA . Our customized secure computation framework is also publicly available at http://github.com/XieConnect/CircuitService Contact: b.malin@vanderbilt.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Background: A long-standing epidemiological puzzle is the reduced rate of rheumatoid arthritis (RA) in those with schizophrenia (SZ) and vice versa. Traditional epidemiological approaches to determine if this negative association is underpinned by genetic factors would test for reduced rates of one disorder in relatives of the other, but sufficiently powered data sets are difficult to achieve. The genomics era presents an alternative paradigm for investigating the genetic relationship between two uncommon disorders. Methods: We use genome-wide common single nucleotide polymorphism (SNP) data from independently collected SZ and RA case-control cohorts to estimate the SNP correlation between the disorders. We test a genotype X environment (GxE) hypothesis for SZ with environment defined as winter- vs summer-born. Results: We estimate a small but significant negative SNP-genetic correlation between SZ and RA (–0.046, s.e. 0.026, P = 0.036). The negative correlation was stronger for the SNP set attributed to coding or regulatory regions (–0.174, s.e. 0.071, P = 0.0075). Our analyses led us to hypothesize a gene-environment interaction for SZ in the form of immune challenge. We used month of birth as a proxy for environmental immune challenge and estimated the genetic correlation between winter-born and non-winter born SZ to be significantly less than 1 for coding/regulatory region SNPs (0.56, s.e. 0.14, P = 0.00090). Conclusions: Our results are consistent with epidemiological observations of a negative relationship between SZ and RA reflecting, at least in part, genetic factors. Results of the month of birth analysis are consistent with pleiotropic effects of genetic variants dependent on environmental context.